Sodium chloride (NaCl) represents the principal component of atmospheric particulates of marine origin. To gain a molecular-level understanding of the adsorption process of water vapor on the NaCl surface, Monte Carlo simulations performed in the Grand Canonical ensemble were carried out, considering the water adsorption at different water pressures on a NaCl(001) surface. The calculated adsorption isotherm shows four different regions, whose coverages correspond to those of the low-, transition-, high-, and pre-solution- coverage regions experimentally observed. Detailed analysis, leveraging unsupervised machine learning for water clusters detection, revealed how the structure of the adsorbed water molecules (islands, layer, and multi- layer) changes depending on water pressure, and how their orientation with respect to the surface varies with the distance from the surface. This detailed information further supports the picture coming from previous experimental IR absorption spectroscopy studies.
Rizza, F., Rovaletti, A., Carbone, G., Miyake, T., Greco, C., Cosentino, U. (2024). Theoretical Investigation of Inorganic Particulate Matter: The Case of Water Adsorption on a NaCl Particle Model Studied Using Grand Canonical Monte Carlo Simulations.. Intervento presentato a: National Congress of Società Chimica Italiana (SCI2024), Milano, Italy.
Theoretical Investigation of Inorganic Particulate Matter: The Case of Water Adsorption on a NaCl Particle Model Studied Using Grand Canonical Monte Carlo Simulations.
Fabio Rizza;Anna Rovaletti;Giorgio Carbone;Toshiko Miyake;Claudio Greco;Ugo Cosentino
2024
Abstract
Sodium chloride (NaCl) represents the principal component of atmospheric particulates of marine origin. To gain a molecular-level understanding of the adsorption process of water vapor on the NaCl surface, Monte Carlo simulations performed in the Grand Canonical ensemble were carried out, considering the water adsorption at different water pressures on a NaCl(001) surface. The calculated adsorption isotherm shows four different regions, whose coverages correspond to those of the low-, transition-, high-, and pre-solution- coverage regions experimentally observed. Detailed analysis, leveraging unsupervised machine learning for water clusters detection, revealed how the structure of the adsorbed water molecules (islands, layer, and multi- layer) changes depending on water pressure, and how their orientation with respect to the surface varies with the distance from the surface. This detailed information further supports the picture coming from previous experimental IR absorption spectroscopy studies.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.